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Home ← Data Integration
Data Integration at CEGIS
The multidisciplinary nature of modern scientific research requires the analysis of complex data from a variety of sources. Data integration is the key by which these data can be brought together and utilized to promote scientific inquiry.
In a 2007 study entitled Facing Tomorrow’s Challenges (USGS Circular 1309), several strategic actions were recommended for USGS consideration in the decade ahead. These include:
- Develop and implement a comprehensive scientific cataloging strategy that combines existing data sets resulting in an integrated science catalog.
- Identify and support authoritative data sources within USGS programs and encourage development and adoption of standards.
- Identify and leverage national and international efforts that promote comprehensive data and information management and foster greater sharing of knowledge and expertise.
- Partner with collaborators and customers to facilitate data integration across worldwide science communities.
- Partner in the development of informatics tools and infrastructure that contribute to the evolving global science computing and collaboration platform.
Towards these ends, the goal of data integration research at CEGIS is to develop methods to combine data from The National Map with selected datasets necessary to support visualization, analysis, modeling and decision-making efforts within the USGS, other government agencies and the general public.
On-Going CEGIS Data Integration Projects 2013 – 2014
Integration of Geophysical Data with The National Map Data
The geophysical data integration project at CEGIS currently involves leveraging large web-based data sets with local surveys and The National Map in order to extend the range and the resolution of such surveys with a minimum expenditure on additional field time. Most recently this has been applied to the North American Gravity Database.
Combining data from a local field survey (red dots) with statistically compatible data from the North American Gravity Data base (blue dots) yields a data set with much higher resolution and areal extent than either data set on its own.
Download Elevation Difference and Bouguer Anomaly Analysis Tool
Integration of Under-utilized USGS Data Sets
In many areas the next generation geo-referenced, digital geologic maps are as yet unavailable. Therefore, in order to integrate geologic data some other means of data acquisition is necessary. Current data integration efforts look toward conflating historic geologic maps for an area (Ste. Genevieve County, MO) and integrating the digital results with The National Map data.
Five geologic maps of various publication dates are conflated with the large scale geologic map of Ste. Genevieve County of Weller and Clarke, 1922. This provides greater detail in areas where smaller scale maps exist with the larger scale data in other areas. These are then integrated with data from The National Map.
Supply Chain Resiliency Modeling
While emergency responses to large-scale natural or man-made disasters are fairly well studies, less attention has been paid to returning an urban environment to its former state of normality (it's resiliency). The most recent start-up project at CEGIS involves integrating geospatial data from The National Map and other sources with infrastructure data and supply chain data to model.
Papers and Posters
Community for Data Integration (CDI),
U.S. Geological Survey Earthcube,
National Science Foundation Homeland Infrastructure Foundation-Level Working Group (HIFLD)
Tom Shoberg, Team Leader
Collaborating Academic Faculty
Dr. Paul Stoddard, Department of Geology and Environmental Sciences, Northern Illinois University - Integration of Geophysical Data
Dr. Suzanna Long, Department of Engineering Management and Systems Engineering, Missouri University of Science and Technology – Supply Chain Resiliency Modeling
Dr. Steven Corns, Department of Engineering Management and Systems Engineering, Missouri University of Science and Technology – Supply Chain Resiliency Modeling
Dr. Hector Carlos, Department of Industrial Engineering, University of Puerto Rico at Mayaguez - Supply Chain Resiliency Modeling